Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem

• URL: http://arxiv.org/abs/2211.13922v1
• Date: Fri, 25 Nov 2022 06:20:11 GMT
• Title: Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem
• Authors: Roberto Garc\'ia-Torres, Alitzel Adriana Macias-Infante, Santiago Enrique Conant-Pablos, Jos\'e Carlos Ortiz-Bayliss and Hugo Terashima-Mar\'in
• Abstract summary: The Capacitated Vehicle Routing Problem is a well-known NP-hard problem that poses the challenge of finding the optimal route of a vehicle delivering products to multiple locations. Recently, new efforts have emerged to create constructive and perturbative Deep Learning-based algorithms to tackle this problem. We propose a Combined Deep Constructor and Perturbator, which combines two powerful constructive and perturbative Deep Learning-based algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Capacitated Vehicle Routing Problem is a well-known NP-hard problem that poses the challenge of finding the optimal route of a vehicle delivering products to multiple locations. Recently, new efforts have emerged to create constructive and perturbative heuristics to tackle this problem using Deep Learning. In this paper, we join these efforts to develop the Combined Deep Constructor and Perturbator, which combines two powerful constructive and perturbative Deep Learning-based heuristics, using attention mechanisms at their core. Furthermore, we improve the Attention Model-Dynamic for the Capacitated Vehicle Routing Problem by proposing a memory-efficient algorithm that reduces its memory complexity by a factor of the number of nodes. Our method shows promising results. It demonstrates a cost improvement in common datasets when compared against other multiple Deep Learning methods. It also obtains close results to the state-of-the art heuristics from the Operations Research field. Additionally, the proposed memory efficient algorithm for the Attention Model-Dynamic model enables its use in problem instances with more than 100 nodes.

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